Abstract:

Strong evidence shows that characteristic patterns of breast tissues as seen on mammography, referred to as mammographic parenchymal patterns, provide crucial information about breast cancer risk. Quantitative evaluation of the characteristic mixture of breast tissues can be used as for mammographic risk assessment as well as for quantification of change of the relative proportion of different breast tissue patterns. This paper investigates mammographic segmentation based on spatial moments and prior information of mammographic building blocks (i.e. nodular, linear, homogenous, and radiolucent) as described by TabaÂ¿r's tissue models to describe parenchymal patterns. The algorithm extracted texture features from a set of sub-sampled mammographic patches. TabaÂ¿r's mammographic building blocks were modelled as statistical distribution of clustered filter responses based on spatial moments. Evaluation was based on the Mammographic Image Analysis Society (MIAS) database. The experimental results indicated that the developed methodology is capable of modelling complex mammographic images and can deal with intraclass variation and noise aspects. The results show realistic segmentation on tissue specific regions with respect to breast anatomy and TabaÂ¿r's tissue models. In addition, the segmentation results were used for mammographic risk based classification of the entire MIAS database resulting in ~70% correct low/high risk classification.